TrainingObserver (TIBCO Patterns Learn Library Documentation)

All Known Subinterfaces:

ConvergenceObserver

All Known Implementing Classes:

COErrorRate, COErrorRateMin, COIterationCount, COScoreChange
```
public interface TrainingObserver
```
Defines an object that gathers training statistics for a certain dataset.

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method	Description
`void`	`beginIteration()`	Resets internal state to prepare for a new iteration of evaluating the entire dataset.
`void`	`endIteration()`	Any actions that need to be performed after evaluating the dataset.
`void`	`evaluatePrediction(VectorExample e)`	Interprets or accumulates prediction result of a single feature vector Called once for each feature vector in the dataset
`boolean`	`hasIterations()`
`boolean`	`isPerfectResult()`

- Method Detail
  - beginIteration
```
void beginIteration()
```
    Resets internal state to prepare for a new iteration of evaluating the entire dataset. Can also be used to report progress before every training iteration.
  - evaluatePrediction
```
void evaluatePrediction(VectorExample e)
```
    Interprets or accumulates prediction result of a single feature vector Called once for each feature vector in the dataset
    
    Parameters:
    
    e - - an example that must have the RLink prediction assigned.
  - endIteration
```
void endIteration()
```
    Any actions that need to be performed after evaluating the dataset. E.g. report progress in GUI.
  - isPerfectResult
```
boolean isPerfectResult()
```
    Returns:
    
    true if the model fits the training data perfectly and therefore its predictions cannot be improved. Any further training with the same data is futile.
  - hasIterations
```
boolean hasIterations()
```
    Returns:
    
    true if any training iterations have been done using this observer.